@inproceedings {,
	title = {Lossy all-to-all exchange for accelerating parallel 3-D FFTs on hybrid architectures with GPUs},
	journal = {2022 IEEE International Conference on Cluster Computing (CLUSTER)},
	year = {2022},
	month = {2022-09},
	pages = {152-160},
	abstract = {In the context of parallel applications, communication is a critical part of the infrastructure and a potential bottleneck. The traditional approach to tackle communication challenges consists of redesigning algorithms so that the complexity or the communication volume is reduced. However, there are algorithms like the Fast Fourier Transform (FFT) where reducing the volume of communication is very challenging yet can reap large benefit in terms of time-to-completion. In this paper, we revisit the implementation of the MPI all-to-all routine at the core of 3D FFTs by using advanced MPI features, such as One-Sided Communication, and integrate data compression during communication to reduce the volume of data exchanged. Since some compression techniques are {\textquoteleft}lossy{\textquoteright} in the sense that they involve a loss of accuracy, we study the impact of lossy compression in heFFTe, the state-of-the-art FFT library for large scale 3D FFTs on hybrid architectures with GPUs. Consequently, we design an approximate FFT algorithm that trades off user-controlled accuracy for speed. We show that we speedup the 3D FFTs proportionally to the compression rate. In terms of accuracy, comparing our approach with a reduced precision execution, where both the data and the computation are in reduced precision, we show that when the volume of communication is compressed to the size of the reduced precision data, the approximate FFT algorithm is as fast as the one in reduced precision while the accuracy is one order of magnitude better.},
	doi = {10.1109/CLUSTER51413.2022.00029},
	author = {Cayrols, Sebastien and Li, Jiali and George Bosilca and Stanimire Tomov and Ayala, Alan and Dongarra, Jack}
}
@conference {,
	title = {Performance Analysis of Parallel FFT on Large Multi-GPU Systems},
	booktitle = {2022 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)},
	year = {2022},
	month = {2022-08},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Lyon, France},
	doi = {10.1109/IPDPSW55747.2022.00072},
	url = {https://ieeexplore.ieee.org/document/9835388/},
	author = {Ayala, Alan and Stanimire Tomov and Stoyanov, Miroslav and Haidar, Azzam and Dongarra, Jack}
}
@conference {,
	title = {Accelerating Multi - Process Communication for Parallel 3-D FFT},
	booktitle = {2021 Workshop on Exascale MPI (ExaMPI)},
	year = {2021},
	month = {2021-12},
	publisher = {IEEE},
	organization = {IEEE},
	address = {St. Louis, MO, USA},
	abstract = {Today largest and most powerful supercomputers in the world are built on heterogeneous platforms; and using the combined power of multi-core CPUs and GPUs, has had a great impact accelerating large-scale applications. However, on these architectures, parallel algorithms, such as the Fast Fourier Transform (FFT), encounter that inter-processor communication become a bottleneck and limits their scalability. In this paper, we present techniques for speeding up multi-process communication cost during the computation of FFTs, considering hybrid network connections as those expected on upcoming exascale machines. Among our techniques, we present algorithmic tuning, making use of phase diagrams; parametric tuning, using different FFT settings; and MPI distribution tuning based on FFT size and computational resources available. We present several experiments obtained on Summit supercomputer at Oak Ridge National Laboratory, using up to 40,960 IBM Power9 cores and 6,144 NVIDIA V-100 GPUs.},
	doi = {10.1109/ExaMPI54564.2021.00011},
	url = {https://ieeexplore.ieee.org/document/9652837/},
	author = {Ayala, Alan and Tomov, Stan and Stoyanov, Miroslav and Haidar, Azzam and Dongarra, Jack}
}
